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The challenge: Imaging is medicine's richest data but its least usable: siloed across PACS, CROs, and central labs, hard to de-identify, and rarely linked to other data modalities that power biomarkers and patient stratification. The hard part is the data, not the model. The takeaway: The frontier isn't a better model, it's the data foundation under it. Govern imaging in one place, make it queryable, de-identify at scale, and link it to EHR, omics, and trial data (via governed lakehouse patterns). Get the data right, and the model becomes the easy part. Part I — The problem Four sectors, one bottleneck Hospitals and health systems generate the raw material. Radiology is the most heavily regulated corner of medical AI anywhere: of the roughly 950 AI/ML-enabled devices the FDA had authorized by mid-2024, about 723 (around 76%) were radiology tools. 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